Susceptibility Zonation Research Articles

Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring the necessity of effective flood management and mitigation efforts. Identifying flood hazard areas is the very first step in achieving such ends. While machine learning algorithms have been widely used in flood susceptibility studies, there is a significant dearth of research addressing their application for local-scale assessment of flood susceptibility in Bangladesh. Besides, in most of the flood susceptibility studies, only statistical metrics are used to evaluate the prediction capability of machine learning algorithms, overlooking their ability to account for spatial consistency. Therefore, this study attempts to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in flash flood susceptibility assessment at local scale using statistical and spatial metrics. RF model performed better than SVM with area under the curve (0.964), accuracy (92.1 %) and kappa (0.894). Five susceptibility zones were identified using a natural breaks method: very high, high, moderate, low and very low. The overall spatial agreement between the susceptibility maps of RF and SVM was 73.3 %. For very low susceptible classes, the spatial agreement was highest for RF-SVM (97.15 %) and SVM-RF (81.54 %). The findings of this study can be useful for a more accurate assessment of flash flood susceptibility in similar local settings. Moreover, the prepared susceptibility map can be helpful for taking sustainable measures to mitigate the devastating effects of flood hazard in northeastern Bangladesh.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The resultsof this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

Assessing the relationship between contributing factors and sinkhole occurrence in Mexico City

Sinkholes pose a significant hazard in Mexico City (CDMX), causing substantial economic damage. While the link between sinkhole formation and groundwater extraction has been studied, specific mechanisms vary by site. Our overall aim is to characterize the phenomenon of sinkholes in CDMX. To achieve this, we create a database with 13 influencing factors, including population density, well density, distance to faults, fractures, roads, streams, elevation, slope, clay thickness, lithology, subsidence rate, geotechnical zones, and soil texture. Sinkhole locations were obtained from CDMX’s Risk Atlas (2017–2019). We shaped a susceptibility map based on statistical regression methods derived from applying linear regression models. For the susceptibility map, results showed that 40% of variables are significantly correlated with sinkhole density. Despite the regression model explained 24% of sinkhole density variability, it helped choosing variables for the susceptibility map that correlate better (89.7%). Hence, we identified that the northeast CDMX was the most susceptible zone. Therefore, the compound assessment of environmental factors is useful for the evaluation of susceptibility maps to identify prone factors for the generation of sinkholes. This framework provides relevant information for better use of the territory throughout the development of public policies.

Redefining landslide susceptibility under extreme rainfall events using deep learning

The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rainfall events (EREs) in the southern Western Ghats (WG) caused widespread landslides across the region. Landslide susceptibility maps generated using the past landslide inventory are one of the efficient tools that can help mitigate the deleterious effects of landslides. Landslide susceptibility maps produced using the landslide inventory of normal and extreme rainfall years by deep neural network (DNN) modelling vary in terms of their degree of susceptibility, and the differences are prominent in the moderate and high susceptibility zones. The DNN model trained by landslide inventory of normal rainfall years is capable of predicting landslides during EREs (with Area Under the Curve (AUC) value >0.90). The inclusion of the landslides that occurred during recent EREs (since 2018) into the existing landslide inventory provides a more accurate and refined prediction of landslide susceptibility, which facilitates risk-informed landscape planning and development of the region. In addition, the result reveals that 13 % of the Kerala state is extremely susceptible for landslide occurrence, among this Idukki, Palakkad, Malappuram, Pathanamthitta, and Wayanad districts are highly vulnerable to the occurrence of landslides. Besides, the study also shows an increase of 3.46 % area in extreme susceptibility zone after the 2018 ERE. The updated landslide susceptibility map of the region may be used as a vital tool for planning landslide mitigation activities in the wake of recurrent EREs and associated landslide occurrences.

Identification of Resistant and Susceptible Sources against Blast Diseases in Finger Millet [Eleusine coracana L. (Gaertn.)

The study was undertaken to evaluate the 490 finger millet (Eleusine coracana L.) genotypes (released, pre-release and breeding genotypes) including two check varieties viz., Indaf 5 (susceptible) and GPU-28 (resistant) against neck and finger blast disease at Hill Millet Research Station, Waghai, The Dangs, Gujarat during kharif, 2018 to 2022. Among all the 490 genotypes, 148 genotypes have shown resistant reaction under high disease pressure field conditions for neck and finger blast disease. However, 44 germplasm genotypes have shown consistent susceptible reaction during five years tested viz; kharif, 2018 to 2022. The genotypes viz., CFMV 2, GN-8, VL-376, PR-202 and GPU-67, showed the higher immune reaction under natural field condition. The pooled percent disease intensity of neck and finger blast diseases ranged from 2.90 to 72.4 per cent and 3.9 to 84.2 per cent respectively, in all tested genotypes including national and local checks. The resistant check variety GPU-28 exhibited resistant reaction to both neck and finger blast. In susceptible check variety Indaf-5 recorded the pooled diseases intensity of 61.74 % for neck blast and 73.6 % for finger blast. In which 72.4 and 84.2 is the highest percent pooled disease intensity of neck and finger blasts recorded in finger millet entry WN 666 which is the most highly susceptible reaction noted across five years of study in blast susceptible zone of Gujarat.

An AHP GIS-based Methodology for the Stability Assessment of the Djebel El Ouahch Collapsees on the Numidian Flysh Formation in Northeast Algeria's Constantine Region

This study presents a comprehensive investigation into the factors underlying the collapse incident that occurred in the Jebel El Ouahch Tunnel in the Numidian flysch, Constantine region, Northeast Algeria. This methodology focuses on evaluating landslide susceptibility through the application of the Analytic Hierarchy Process (AHP) along the reconfigured path of the collapsed A1 highway tunnel section in the Constantine region of northeastern Algeria. Various influential factors contributing to landslides were analyzed, including lithofacies, slope gradient, slope aspect, elevations, fault density, plan curvature, distance from streams, and distance from roads. Utilizing a Geographic Information System (GIS), these eight causative factors were prepared for assessment. The findings indicate that slope gradient and lithology play pivotal roles as primary controlling factors in landslide susceptibility. The model exhibited a commendable success rate of 93% in predicting landslide susceptibility, as demonstrated by the area under the curve (AUC) plot generated from the landslides susceptibility map. Most of the new road falls within the highly susceptible area to landslides. This validated model can serve as an effective tool for mapping landslide susceptibility zones along the newly established road path following the collapsed tunnel. Moreover, its applicability extends to similar environments, showcasing its potential as a valuable resource for hazard assessment and planning in comparable terrains. The deviation road, as well as tunnel T01 of the A1 highway, is in a state of proven instability. It is certain that they will experience continuous, recurrent, and intense landslides. A radical solution to all the geotechnical issues plaguing this section of the highway is to reroute it far away from the Subnumidian formations. A deeper comprehension of the geological and geotechnical intricacies in challenging terrains can significantly enhance the safety and reliability of transportation networks in these demanding contexts.

Analysis of Disaster Vulnerability Areas in West Bandung Regency, West Java, Indonesia

Mineral resources, coal, and rock are one of the potentials possessed by Indonesia to be able to earn income from the mining sector. West Bandung Regency is one of the areas that have quite a lot of potential rock and mineral resources. The potential of this area is quite large, namely andesite rock, sand, limestone, and sandstone. Zeolite and others. On the other hand, the West Bandung area has a high potential and threat of being a disaster-prone. West Bandung Regency has the highest number of natural disasters occurring in the type of landslide disaster, which occurred 52 times in the period 2008-2016, or 68% of all disasters. Loss of economic value also occurs for mining material resources which are limited by the existence of a disaster zone. So it is necessary to carry out mitigation from the start to map disaster areas that have an impact on the distribution and existence of mining material resources. This study aims to identify and analyze the potential of rock resources in disaster-prone areas, so as to be able to prioritize conservation aspects for potential mining materials. The method used in this research is through literature study, mapping the potential of mining materials, mapping the potential of disaster-prone areas, processing of secondary data, and analysis using remote sensing. The results of this study are that the rocks in the West Bandung area are divided into groups of volcanic rocks, sedimentary rocks, and alluvial deposits. The volcanic rock group got a score of 3 because it was considered more prone to erosions than the sedimentary and alluvial rock groups which were scored 2 and 1. with a weighted level of disaster vulnerability. The zone of high disaster susceptibility is considered to have the highest probability of a disaster occurring. Therefore, in the final result, the overlap between the distribution of the potential for minerals and the zone of high disaster susceptibility results in a potential area for minerals that are relatively safe from disasters, both soil movement, and flooding. Potential mining resources in West Bandung Regency are Andesite basalt 1,860,412 ha (1.43%), Limestone 667.05 ha (0.50%), Sirtu 40,949.76 ha (31.35%%).

Prediction of soil erosion and sediment yield in an ungauged basin based on land use land cover changes.

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management.

Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

AbstractClimate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.

Landslide susceptibility zoning based on Rock Engineering System application to the Tehran case study

AbstractLandsliding and slope instability pose significant risks to human settlements and infrastructure. This study aims to develop a landslide susceptibility map for the mountainous regions of Tehran province, Iran. The rock engineering approach was employed at a regional scale, utilizing a database of regional landslides. The semi‐quantitative Rock Engineering System method was applied, considering 10 conditioning factors. The model was validated using historical landslide data, showing strong correlation. The majority of landslides occurred within medium and high susceptibility zones. Geological units, particularly the Karaj Formation's claystone layers, displayed a clear association with landslide susceptibility. The rock engineering approach yielded a highly accurate susceptibility map. Findings can inform future territorial planning in the area.

Effect of microstructure on hydrogen embrittlement and hydrogen-induced cracking behaviour of a high-strength pipeline steel weldment

Hydrogen embrittlement (HE) and hydrogen-induced cracking (HIC) behaviour of a X65 steel pipeline weldment were investigated using slow strain rate tensile (SSRT) testing of specimens that were specifically extracted from different zones of the weldment (i.e., weld metal (WM), heat-affected zone (HAZ), and base metal (BM)). The WM was found to be the most susceptible zone to HE and HIC, while BM the least. Analysis of microstructure, fracture surface, secondary crack formation, and mechanical behaviour revealed that the high HE susceptibility of WM is correlated to microstructural features including Ti-rich inclusions, martensite/austenite (M/A) constituents, and prior austenite grain boundaries (PAGBs).

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9–12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

A REVIEW OF STATISTICAL APPROACHES USED FOR LANDSLIDE SUSCEPTIBILITY ANALYSIS WITH THE HELP OF REMOTE SENSING AND GIS TECHNOLOGY

One of the most significant environmental risks is landslides. It might happen due to human action or natural occurrence and have significant social, economic, and environmental repercussions. The danger of landslides occurring in a certain area based on regional topographical features is known as landslide susceptibility. Numerous articles evaluating landslide susceptibility have been written since the middle of the seventies utilizing a variety of techniques and methodologies in diverse geographical and environmental situations.To critically review this literature, major factors that are considered for landslide susceptibility, and the models that are used for landslide susceptibility analysis. Then these data are represented in a graphical visualization form.Landslide susceptibility zoning, The goal of the current study, which was undertaken as a review, was to provide a critical analysis of statistical methods used for landslide susceptibility modelling and associated terrain zonation from 2010 to 2023.There has been an increase in the number of publications from 2019 onwards on the topic of landslide susceptibility. In 20.8% of the studies, the FR model and LR model has been chosen as the most popular approach for determining landslide vulnerability. Other then Logistic regression and Frequency ratio model, other models like Fuzzy, WoE, ANN, SVM and RF are also popular for landslide susceptibility. As the technology in the field of GIS&RS is updating day to day and recent policy developments, are significant factors in boosting interest in landslide susceptibility research.

Geospatial delineation of flood susceptible zones using analytical hierarchy process

Demarcation of flood-prone areas based on risk level plays a key role in flood management systems. Geospatial approach has gained much significance among the researcher working in flood risk management. This work incorporated an analytic hierarchy process (AHP) and geographic information system (GIS) to delineate the flood susceptible zones of the lower Damodar River basin. Various influencing factors were initially assigned with weights with respect to their relative importance towards flood susceptibility and integrated into GIS platform by weighted overlay approach. Based on the current study area, the flood zone map was sub-classified into four zones, namely critical, high and low, and safe zone, respectively. Moreover, sensitivity analysis was also performed to evaluate the most significant factors in flood susceptibility using the exclusion of factors from the estimation of flood zone index. Sensitivity analysis showed that River distance and elevation are the most sensitive factors for evaluating flood zonation.

A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets

Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By comparing the classification performance and predicted probability distributions across multiple unbalanced datasets with known and unknown sample proportions, this study assesses the landslide susceptibility model’s generalization ability in the context of class-prior shifts. The study investigates the potential of Bagging PU Learning, a semi-supervised learning approach, in improving the generalization performance of landslide susceptibility models and proposes the Bagging PU-GDBT algorithm. Our findings highlight the effectiveness of Bagging PU Learning in enhancing the recall of landslides and the generalization capabilities of models on unbalanced datasets. This method reduces prediction uncertainties, especially in high and very high susceptibility zones. Furthermore, results emphasize the superiority of models trained on balanced datasets with 1:1 sample ratio for landslide susceptibility mapping over those trained on unbalanced datasets.

GIS-based landslide and rockfall susceptibility zoning in Chepelarska River Basin (Western Rhodope Mountains, Bulgaria)

Landslides and rockfalls are one of the most widespread hazardous phenomena in the Western Rhodopes, Bulgaria. These mass-wasting types occur together with other dangerous phenomena such as river bank erosion and soil degradation, and can cause a lot of infrastructural damage. The main purpose of this paper is to perform GIS-based landslide and rockfall susceptibility zoning and to elaborate susceptibility maps at a scale of а river basin on the example of the Chepelarska River Basin (RB). In order to achieve this goal, nine causal factors have been selected, and map of landslides and field study have been used. These factors were reclassified using histograms and the natural breaks method. In addition, the frequency ratio values of each class have been calculated. To obtain the causal factors impact on sliding and rockfalls, the weights and weighted averages sum of their classes were calculated and analysed. For the complex analysis and elaboration of mass-wasting susceptibility maps, weighted overlay and weighted sum methods have been applied. The obtained results show that from the total 38 landslides and rockfalls in the study area, 26 of them fall into the very high susceptibility zone applying a weighted overlay, and 25 of them fall into the same zone using the weighted sum method. These results and the used methods could be used for other small and medium-sized river basins.

A research on a new mapping method for landslide susceptibility based on SBAS-InSAR technology

The acquisition of landslide inventory represents a pivotal challenge in landslide susceptibility mapping. Existing landslide susceptibility maps(LSMs) predominantly rely on manually obtained landslide inventories, leading to an overdependence on expert insights and susceptibilities to topographic and geomorphic influences. In regions characterized by steep terrain, obtaining a landslide inventory can be arduous or even unattainable, subsequently constraining the utility of LSMs. Addressing the limitations of conventional LSMs, this study introduces an innovative method for landslide inventory compilation and LSM creation, utilizing Small Baselines Subset Interferometry Synthetic Aperture Radar(SBAS-InSAR) technology. The study area selected for illustration is the Dongchuan district, notorious for frequent landslide occurrences. The application of SBAS-InSAR facilitated the extraction of surface deformation data, subsequently enabling the selection of landslide deformation points as samples. These samples underwent analysis through a particle swarm optimization-backpropagation neural network(PSO-BPNN) guided by deformation thresholds and the landslide developmental environment. This produced the LSM for the Dongchuan district. Subsequent validation of the LSM employed both qualitative and quantitative measures. Results elucidate that the LSM, as derived from the presented approach, primarily highlights high to very high susceptibility zones in landslide-prone areas, mirroring the spatial distribution of historical landslides. The method also achieved a commendable accuracy(ACC) of 79.59% and an area under the curve(AUC) value of 0.88. Notably, the landslide density exhibited a direct correlation with increasing susceptibility class. Such findings align with previous studies, endorsing the feasibility and reliability of the proposed approach.

Influences of non-landslide sample selection strategies on landslide susceptibility mapping by machine learning

Landslide susceptibility mapping is crucial in mitigating the risk of regional landslide hazards. The main objectives of this study are to design and analyze the impact of three non-landslide data generation methods on landslide susceptibility mapping. The specific non-landslide data selection methods are the random generation of non-landslide points through different distance landslide buffers, by selecting areas with low landslide influence in the landslide condition factor layer, and based on the results of the information value model partitioning by selecting low to medium susceptibility zones. There were 14 landslide condition factors used for the landslide susceptibility mapping, and correlations between landslides and condition factors were analyzed using the Gini index. The 70% dataset was modelled using a logistic regression model as well as an artificial neural network model. Finally, statistical metrics and AUC values were used for the 30% validation data for comparing the predictive performances of different non-landslide data generation strategies in the model. Based on the validation results, models A6 and B6 performed the best, with respective AUC values of 0.997 and 0.995. The findings show that the non-landslide samples generated using the low susceptibility interval of the information value model have the best performance.

Landslide susceptibility mapping for the Red Sea Mountains: A multi-criteria decision analysis approach

The Earth's climate system is clearly warming. Unquestionably, climate change has an impact on landslides by affecting the stability of both naturally occurring and artificially constructed slopes. Less certain are the type, extent, degree, and direction of the changes in stability conditions as well as the location, abundance, activity, and frequency of landslides in response to the expected climate changes. Climate change threatens global ecosystems, causing severe human and economic losses through sea level rise, hurricanes, flooding, landslides, and heat waves. Landslides in high mountains could become more common as a result of these changes. The high-speed electric train project in Egypt seeks to provide sustainable transportation, lessen climate change, and enhance public health. The study area was conducted along a 70-km track in the Eastern Desert, Egypt, across steep and rugged basement rocks of the Red Sea mountain chain (RSM). The geographic pinpointing of landslide susceptibility zones plays a significant role in promoting hazards mitigation in mountainous areas, such as RSM. The aim of this research project is to create a landslide susceptibility map (LSM) utilizing multi-criteria decision analysis (MCDA) in the region using information value (IV) and analytical hierarchy process (AHP) approaches that encompass the third proposed track of the high-speed electric railroad Qena-Hurghada between the RSM. The landslide inventory map, which has a total of 12 single landslide locations, was created based on fieldwork using hand-held GPS and Landsat 8 images. The two decision-making techniques were used to contrast the LSM factors based on the knowledge of experts using the qualitative method of AHP with the statistical approach that depends on the field data IV. Ten landslide-influencing parameters taken into consideration are slope, lithology, rainfall distribution, distance to fault, distance to track, seismicity of area, distance to epicenter, land cover, distance to stream, and aspect. The parameters were obtained using LANDSAT8 and SRTM DEM of a 30 m resolution, vectorized lithological structure units from a 1:2000, 000 map, and data between (1913–2000) for yearly precipitation and (1997–2018) for seismic activity of the research area. A GIS-based method produces a themed layer map for each geoenvironmental element, weighing each parameter after examination. A LSM was produced using a combination of thematic layers that were then separated into four grades: low, moderate, high, and very high, based on the intensity of the landslide vulnerability. According to AUC data, the IV model has a better success rate 0.85 than the AHP model 0.82. The study's models, especially when applied to the IV method, showed good accuracy in forecasting landslide vulnerability. The method shows rising landslide percentages with susceptibility and delivers accurate results that are compatible with the landslide inventory map. Environmental decisions often lead to unintended consequences due to lost information, incorrect factor selection, limited expert expertise, and ignoring uncertainty. This investigation emphasizes the importance of LSM as a tool for decision-makers to minimize landslide losses and damage.

GIS-Based Landslide Susceptibility Zonation Mapping Using the Weighted Information Model in Erlang Mountain - Zheduo Mountain Power Transmission Project, China

GIS-Based Landslide Susceptibility Zonation Mapping Using the Weighted Information Model in Erlang Mountain - Zheduo Mountain Power Transmission Project, China